PROJECT SUMMARY The symptoms of schizophrenia (SCZ) compromise major aspects of a patient's life and that of their families, and the etiology of the disease is still unknown. There is agreement on the role of genetic vulnerability, but the risk genes are so many that it is difficult to explain by which mechanisms they confer liability to SCZ. Recent evidence shows that the co-regulation of gene expression may be a mechanism of SCZ risk, i.e., the concerted action of genes, more than the function of individual genes, may be critical for SCZ etiology. This project proposal stems from two research questions. First, how gene co-expression networks unfold across the lifespan. Computational advancements have greatly increased the accuracy of RNA sequencing measures, and it is now possible to investigate the variation of gene co-expression over the lifespan. The underlying hypothesis is that genetic variation is translated into pathophysiology via molecular alteration of neurodevelopmental trajectories. Second, most existing gene coexpression studies involve brain tissue homogenates, which contain many cell types with likely differing co-expression patterns. Instead, laser capture microdissection (LCM) allows isolating distinct cellular populations with intact cell bodies prior to RNA quantification, thus affording relative cell-specificity. Several biological functions affected in SCZ involve neuronal genes, hence gene expression in neurons may reveal co-expression patterns with greater relevance to the pathophysiology of SCZ than tissue homogenates. This project will identify and resolve the technical challenges of studying the course of temporally co-regulated gene expression (Aim 1) and provide preliminary evidence for its feasibility in neuronal populations from multiple brain regions in a canonical circuit linking hippocampus with prefrontal cortex (Aim 2). Aim 1. Preliminary data from RNAseq analysis of dorsolateral prefrontal (DLPFC) homogenate tissue show the feasibility and potential of the approach; however, these techniques have not yet been applied at a cell-specific level. Two additional available datasets, i.e., hippocampal homogenate tissue and dentate gyrus cells (DG) obtained via LCM, will allow for further insight and technical development. The comparison of DLPFC with hippocampal homogenate data will reveal whether archicortical structures present specific challenges to the assumptions of gene co-expression network analysis; comparing hippocampal homogenate with DG data will ensure that LCM affords sufficient signal to noise ratio to compute valid dynamic networks in a homogeneous cell population. Aim2. This project will study 20 individuals, ten patients with SCZ and ten neurotypical subjects, as a proof of concept of the feasibility and importance of studying single cell gene co-expression patterns. This approach will be applied to neurons from the CA1 of the hippocampus, the subiculum and the DLPFC, regions monosynaptically connected to form a critical brain circuit implicated in SCZ. Building on the evidence collected in this project proposal, future studies will use the laboratory protocols developed here to collect a unique larger dataset of LCM-based RNA sequencing data in developmental ages from SCZ-associated brain circuits.